Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record

Abstract

Background and aims: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification methods. The present study sought to design a classification algorithm for NAFLD within the electronic medical record (EMR) for the development of large-scale longitudinal cohorts.

Methods: We implemented feature selection using logistic regression with adaptive LASSO. A training set of 620 patients was randomly selected from the Research Patient Data Registry at Partners Healthcare. To assess a true diagnosis for NAFLD we performed chart reviews and considered either a documentation of a biopsy or a clinical diagnosis of NAFLD. We included in our model variables laboratory measurements, diagnosis codes, and concepts extracted from medical notes. Variables with P < 0.05 were included in the multivariable analysis.

Results: The NAFLD classification algorithm included number of natural language mentions of NAFLD in the EMR, lifetime number of ICD-9 codes for NAFLD, and triglyceride level. This classification algorithm was superior to an algorithm using ICD-9 data alone with AUC of 0.85 versus 0.75 (P < 0.0001) and leads to the creation of a new independent cohort of 8458 individuals with a high probability for NAFLD.

Conclusions: The NAFLD classification algorithm is superior to ICD-9 billing data alone. This approach is simple to develop, deploy, and can be applied across different institutions to create EMR-based cohorts of individuals with NAFLD.

Keywords: Electronic medical records; Nonalcoholic fatty liver disease; Nonalcoholic steatohepatitis; Triglycerides.

Figure 1

ROC Curves comparing the Performance of the Codified and Narrative Model for NAFLD to the ICD-9 Coding Data Model for NAFLD